Senior MLOps Engineer (GCP / CV / Perception Pipelines)

Jaipur Robotics SA; Schweiz; Vor Ort; CHF 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Competitive salary

Stock options

Zusammenfassung

A clean-tech startup based in Switzerland is seeking a Senior MLOps Engineer to design and operate the infrastructure for their ML systems. This role involves productionizing computer vision and perception pipelines on GCP and building end-to-end ML training and inference pipelines. The ideal candidate will have strong experience with GCP, CI/CD pipelines, Docker, and Kubernetes. A competitive salary and stock options are offered, along with the opportunity to work in an expanding engineering team.

Qualifikationen

• Strong experience with GCP including Cloud Run and GKE.
• Experience building CI/CD pipelines.
• Strong programming skills in Python.

Aufgaben

• Build and maintain CI/CD pipelines using GitHub Actions.
• Automate model training, validation, and deployment workflows.
• Deploy and manage workloads on Google Kubernetes Engine (GKE).

Kenntnisse

GCP (Cloud Run, GKE, GCS, Pub/Sub, IAM)

CI/CD pipelines (GitHub Actions)

Docker and Kubernetes

Data pipelines (Apache Beam / Dataflow)

ML lifecycle understanding

Python programming

Tools

Apache Beam

Google Kubernetes Engine (GKE)

Jobbeschreibung

Full-time

Who we are

At Jaipur Robotics, we build AI systems that turn visual and sensor data into automation, efficiency, and operational intelligence for the waste industry. We are a fast-growing, VC-backed clean-tech startup based in Switzerland, working with leading operators across Europe and expanding our engineering team to develop industrial perception and automation systems.

What we offer

We’re hiring a Senior MLOps Engineer (GCP / Computer Vision & ML Pipelines) to design and operate the infrastructure behind our ML systems.

This role focuses on productionizing computer vision and perception pipelines at scale on GCP. You will work across CI/CD, cloud infrastructure, and data pipelines , ensuring models and data systems run reliably at scale.

• Build and operate end-to-end ML training / inference pipelines
• Work directly with founders and R&D engineers on core systems
• Contribute to scaling real-world AI systems used in industrial environments
• Competitive salary and stock options

Key Responsibilities

MLOps & CI/CD

• Build and maintain CI/CD pipelines using GitHub Actions
• Automate model training, validation, and deployment workflows
• Manage versioning of models, datasets, and pipelines
• Implement safe deployment strategies (rollbacks, staged releases)
• Deploy and manage services on Cloud Run, GCS, Pub/Sub, and data storage systems (SQL / NoSQL / Redis)
• Build scalable pipelines using Apache Beam / Dataflow
• Process large-scale image and sensor datasets
• Ensure reliability through monitoring, observability, and cost-aware design
• Build and manage Docker-based services for ML and data pipelines
• Deploy and manage workloads on Google Kubernetes Engine (GKE)
• Optimize containers for performance, resource efficiency, and reliability
• Implement rolling deployments, health checks, and failover strategies
• Maintain reproducible environments across dev, staging, and prod
• Work closely with ML engineers to productionize models
• Optimize inference pipelines and resource utilization
• Implement monitoring for model performance and drift

Requirements

• Strong experience with GCP (Cloud Run, GKE, GCS, Pub/Sub, IAM)
• Experience building CI/CD pipelines (GitHub Actions or similar)
• Experience with Docker and Kubernetes (GKE) in production
• Experience building data pipelines (Apache Beam / Dataflow)
• Solid understanding of ML lifecycle
• Familiarity with streaming pipelines and real-time systems
• Experience operating in production with failure handling and debugging
• Strong programming skills in Python

Nice to Have

• Experience working in an early-stage startup / scale-up (<50 engineers)
• Experience with camera and LiDAR systems
• Experience deploying on edge in restricted IT/OT industrial environments
• Maintain infrastructure using Terraform (infrastructure-as-code)